Load points CSV and plot. Then calculate pairwise geographic distances.
Mother | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
1 | 0.0000 | 589.9182 | 557.8474 | 548.0584 | 833.8242 | 395.6836 | 486.1634 | 627.3924 | 1,143.6408 | 2,303.4655 | 2,107.8114 | 1,585.1244 | 2,120.0934 | 2,316.5406 | 2,207.9074 |
2 | 589.9182 | 0.0000 | 526.0392 | 700.1976 | 663.6468 | 931.1458 | 907.6392 | 1,211.2011 | 1,722.1728 | 2,847.5562 | 2,619.1598 | 2,078.6413 | 2,592.3543 | 2,843.1304 | 2,760.1851 |
3 | 557.8474 | 526.0392 | 0.0000 | 1,003.5932 | 277.2858 | 945.1789 | 1,039.3516 | 1,103.1416 | 1,445.6970 | 2,463.3950 | 2,197.5967 | 1,651.3274 | 2,136.7694 | 2,435.2607 | 2,390.7034 |
4 | 548.0584 | 700.1976 | 1,003.5932 | 0.0000 | 1,248.6171 | 523.6645 | 348.6719 | 852.0066 | 1,482.3075 | 2,686.0277 | 2,540.8705 | 2,048.7506 | 2,590.4755 | 2,726.2964 | 2,576.9796 |
5 | 833.8242 | 663.6468 | 277.2858 | 1,248.6171 | 0.0000 | 1,222.4587 | 1,311.9507 | 1,371.8170 | 1,666.2735 | 2,612.3270 | 2,321.4869 | 1,779.6740 | 2,229.3627 | 2,567.3094 | 2,550.0373 |
6 | 395.6836 | 931.1458 | 945.1789 | 523.6645 | 1,222.4587 | 0.0000 | 218.8407 | 331.7977 | 958.6564 | 2,162.7126 | 2,026.0942 | 1,550.0883 | 2,090.9991 | 2,205.7551 | 2,053.3225 |
7 | 486.1634 | 907.6392 | 1,039.3516 | 348.6719 | 1,311.9507 | 218.8407 | 0.0000 | 519.6035 | 1,155.7855 | 2,360.3863 | 2,236.1822 | 1,766.8553 | 2,307.1584 | 2,410.0629 | 2,247.8787 |
8 | 627.3924 | 1,211.2011 | 1,103.1416 | 852.0066 | 1,371.8170 | 331.7977 | 519.6035 | 0.0000 | 636.2544 | 1,840.7897 | 1,724.0437 | 1,277.5283 | 1,811.5890 | 1,892.0212 | 1,728.6709 |
9 | 1,143.6408 | 1,722.1728 | 1,445.6970 | 1,482.3075 | 1,666.2735 | 958.6564 | 1,155.7855 | 636.2544 | 0.0000 | 1,204.9262 | 1,102.4012 | 727.5280 | 1,225.1060 | 1,257.7926 | 1,094.6738 |
10 | 2,303.4655 | 2,847.5562 | 2,463.3950 | 2,686.0277 | 2,612.3270 | 2,162.7126 | 2,360.3863 | 1,840.7897 | 1,204.9262 | 0.0000 | 373.3146 | 842.9092 | 639.6446 | 181.6337 | 131.6188 |
11 | 2,107.8114 | 2,619.1598 | 2,197.5967 | 2,540.8705 | 2,321.4869 | 2,026.0942 | 2,236.1822 | 1,724.0437 | 1,102.4012 | 373.3146 | 0.0000 | 546.2707 | 282.8608 | 254.5773 | 409.9716 |
12 | 1,585.1244 | 2,078.6413 | 1,651.3274 | 2,048.7506 | 1,779.6740 | 1,550.0883 | 1,766.8553 | 1,277.5283 | 727.5280 | 842.9092 | 546.2707 | 0.0000 | 541.8295 | 787.7837 | 802.5889 |
13 | 2,120.0934 | 2,592.3543 | 2,136.7694 | 2,590.4755 | 2,229.3627 | 2,090.9991 | 2,307.1584 | 1,811.5890 | 1,225.1060 | 639.6446 | 282.8608 | 541.8295 | 0.0000 | 483.6323 | 691.4241 |
14 | 2,316.5406 | 2,843.1304 | 2,435.2607 | 2,726.2964 | 2,567.3094 | 2,205.7551 | 2,410.0629 | 1,892.0212 | 1,257.7926 | 181.6337 | 254.5773 | 787.7837 | 483.6323 | 0.0000 | 289.2047 |
15 | 2,207.9074 | 2,760.1851 | 2,390.7034 | 2,576.9796 | 2,550.0373 | 2,053.3225 | 2,247.8787 | 1,728.6709 | 1,094.6738 | 131.6188 | 409.9716 | 802.5889 | 691.4241 | 289.2047 | 0.0000 |
Load cleaned CSVs
dm<-read.csv("/Users/Kaushiknarasimhan/Dropbox/Phd-MacBook Pro/Ch3/full distance matrix.csv")%>%
select(1:3)
d<-read.csv("~/Dropbox/Phd-MacBook Pro/Ch3/CH3 analysis/DataSheetPreScan.csv")
s<-read.csv("~/Dropbox/Phd-MacBook Pro/Ch3/CH3 analysis/Scans.csv")%>%
select(!c(OCR, StdDev:MaxThr))full_join(d,s,by=c("Trimmed"="M3"))%>%
select(!starts_with("X"))%>%
select(!c(Seq.Ord:Esp))%>%
mutate(Block=as.numeric(str_match(Trimmed,"B(\\d++)")[,2]),#split block,adult,soil
Mother=as.numeric(str_match(Trimmed,"A(\\d++)")[,2]),
Soil=as.numeric(str_match(Trimmed,"S(\\d++)")[,2]),
Alive=if_else(is.na(altura..cm.),0,1),
Alive=as.factor(Alive))%>%
relocate(Block:Alive,.after= Trimmed)%>%
left_join(dm)%>%
select(!starts_with("X"))%>%
select(!c(Raices.en.Ctab.,Scan.,En.bolsa.,Horno..,Scan.Name))%>%
filter(!is.na(Mother))%>%
mutate( Mother=as.factor(Mother),
Soil=as.factor(Soil),
Block=as.factor(Block))%>%
rename(Stem.Diameter=`diámetro..mm.`,
Stem.Height=`altura..cm.`,
WW.above=Peso.Encima,
WW.below=Peso.Abajo)%>%
relocate(all_of(c("Dead.Muerta","Germinado.","NOTAS","Notes")),.after= Distance)%>%
view()->Master
#write.csv(Master,file = paste0("WithScans",Sys.time(),".csv")) Master%>%filter(Alive==1)%>%
summarise(n())## n()
## 1 1270
1270 germinated seeds that were harvested. What is the distribution of seeds that have dry measurements? Looks like blocks 2-7 and 10 are fine. 183 samples were burnt.
## n()
## 1 183
| Mother | Stem.Diameter_mean | Stem.Diameter_sd | Stem.Height_mean | Stem.Height_sd | WW.below_mean | WW.below_sd | WW.above_mean | WW.above_sd | DW.above_mean | DW.above_sd | DW.below_mean | DW.below_sd | Area_mean | Area_sd | Distance_mean | Distance_sd | SurvRate_mean | SurvRate_sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3.696 | 0.738 | 3.831 | 0.721 | 2.708 | 0.300 | 1.076 | 0.386 | 0.206 | 0.092 | 0.861 | 0.204 | 1.927 | 0.959 | 1224.850 | 840.129 | 0.2857143 | 0.1511858 |
| 2 | 4.151 | 0.814 | 4.393 | 1.328 | 3.178 | 0.444 | 1.508 | 0.613 | 0.282 | 0.126 | 1.123 | 0.141 | 2.483 | 1.099 | 1527.224 | 1001.414 | 0.4600000 | 0.2097618 |
| 3 | 4.865 | 0.455 | 5.278 | 1.078 | 3.972 | 0.587 | 2.266 | 0.865 | 0.406 | 0.181 | 1.335 | 0.294 | 4.235 | 1.890 | 1363.799 | 852.154 | 0.4571429 | 0.1554858 |
| 4 | 6.248 | 0.734 | 6.959 | 0.838 | 7.613 | 0.630 | 4.304 | 1.040 | 0.816 | 0.232 | 2.719 | 0.299 | 8.038 | 2.101 | 1455.913 | 977.312 | 0.6866667 | 0.1597617 |
| 5 | 6.971 | 0.461 | 7.843 | 0.873 | 7.416 | 0.699 | 5.552 | 0.919 | 1.119 | 0.194 | 2.352 | 0.300 | 10.917 | 1.949 | 1504.343 | 834.839 | 0.8666667 | 0.1046536 |
| 6 | 6.319 | 0.594 | 7.217 | 0.697 | 5.549 | 0.507 | 4.125 | 0.834 | 0.832 | 0.220 | 1.591 | 0.221 | 8.135 | 2.091 | 1171.965 | 787.764 | 0.5933333 | 0.2218966 |
| 7 | 4.919 | 0.550 | 5.295 | 0.756 | 4.656 | 0.443 | 2.291 | 0.754 | 0.412 | 0.134 | 1.541 | 0.184 | 3.921 | 1.503 | 1285.652 | 870.664 | 0.7200000 | 0.1971222 |
| 8 | 6.338 | 0.784 | 6.714 | 1.726 | 7.471 | 0.593 | 4.453 | 1.129 | 0.906 | 0.229 | 2.478 | 0.306 | 8.290 | 2.681 | 1125.899 | 609.388 | 0.6733333 | 0.2086236 |
| 9 | 6.040 | 0.650 | 6.683 | 0.938 | 6.538 | 0.580 | 3.721 | 0.979 | 0.773 | 0.301 | 2.220 | 0.356 | 6.766 | 2.379 | 1118.418 | 429.208 | 0.7733333 | 0.1579632 |
| 10 | 6.096 | 0.815 | 6.441 | 0.749 | 6.395 | 0.813 | 3.676 | 1.040 | 0.683 | 0.193 | 2.111 | 0.294 | 6.690 | 2.095 | 1504.942 | 1055.760 | 0.7000000 | 0.2236068 |
| 11 | 5.531 | 0.809 | 5.944 | 1.333 | 6.274 | 0.949 | 3.099 | 1.056 | 0.620 | 0.326 | 2.149 | 0.488 | 5.633 | 2.323 | 1379.354 | 973.555 | 0.4466667 | 0.2199567 |
| 12 | 5.168 | 0.862 | 5.758 | 0.745 | 5.195 | 0.721 | 2.679 | 0.871 | 0.519 | 0.187 | 2.008 | 0.310 | 4.579 | 1.490 | 1196.299 | 631.776 | 0.6266667 | 0.1791514 |
| 13 | 7.167 | 0.662 | 7.704 | 0.474 | 7.224 | 0.665 | 5.287 | 0.846 | 1.096 | 0.254 | 2.319 | 0.265 | 10.438 | 2.204 | 1447.049 | 921.030 | 0.7000000 | 0.1195229 |
| 14 | 5.543 | 0.887 | 5.740 | 0.727 | 5.063 | 0.514 | 2.719 | 0.899 | 0.532 | 0.198 | 1.620 | 0.420 | 5.043 | 2.442 | 1410.901 | 1040.914 | 0.3000000 | 0.1568929 |
| 15 | 4.473 | 1.162 | 4.830 | 1.435 | 4.042 | 1.815 | 2.043 | 1.864 | 0.364 | 0.325 | 1.421 | 0.382 | 3.940 | 4.112 | 1551.922 | 965.868 | 0.2642857 | 0.1336306 |
Looks like the same pattern as before. Def some variation between
mothers
Soil appears to have more of an effect when accounting for biomass
Soil appears to have more of an effect when accounting for biomass
Nothing pops out to me here.
PCA | Dimension | Contribution | Variance Explained | Std. Deviaton |
1 | Dim.1 | 4.1034989 | 82.069978 | 2.0257095 |
1 | Dim.2 | 0.4245234 | 8.490469 | 0.6515546 |
2 | Dim.1 | 4.9570210 | 70.814586 | 2.2264369 |
2 | Dim.2 | 1.2084772 | 17.263960 | 1.0993076 |
3 | Dim.1 | 4.9515978 | 70.737111 | 2.2252186 |
3 | Dim.2 | 1.1901776 | 17.002538 | 1.0909526 |
Response | term | statistic | df | p.value |
Below-ground Dry Weight | (Intercept) | 0.336 | 1 | 0.562 |
Mother | 391.804 | 14 | 0.0 | |
Soil | 15.242 | 14 | 0.362 | |
Distance | 0.364 | 1 | 0.546 | |
Mother:Soil | 195.086 | 191 | 0.405 | |
Above-ground Dry Weight | (Intercept) | 4.338 | 1 | 0.0 |
Mother | 289.568 | 14 | 0.0 | |
Soil | 20.863 | 14 | 0.105 | |
Distance | 2.424 | 1 | 0.119 | |
Mother:Soil | 232.561 | 191 | 0.0 | |
Below-ground Wet Weight | (Intercept) | 18.819 | 1 | 0.0 |
Mother | 919.039 | 14 | 0.0 | |
Soil | 37.113 | 14 | 0.0 | |
Distance | 0.216 | 1 | 0.642 | |
Mother:Soil | 313.259 | 195 | 0.0 | |
Above-ground Wet Weight | (Intercept) | 13.075 | 1 | 0.0 |
Mother | 903.595 | 14 | 0.0 | |
Soil | 52.403 | 14 | 0.0 | |
Distance | 0.141 | 1 | 0.708 | |
Mother:Soil | 391.271 | 195 | 0.0 | |
Total Biomass (all samples) | (Intercept) | 3.309 | 1 | 0.069 |
Mother | 701.285 | 14 | 0.0 | |
Soil | 16.920 | 14 | 0.260 | |
Distance | 0.177 | 1 | 0.674 | |
Mother:Soil | 203.536 | 191 | 0.254 | |
Total Biomass (no burnt samples) | (Intercept) | 3.591 | 1 | 0.058 |
Mother | 689.251 | 14 | 0.0 | |
Soil | 17.365 | 14 | 0.237 | |
Distance | 0.261 | 1 | 0.610 | |
Mother:Soil | 201.761 | 191 | 0.283 |
Eck had a categorical variable with either “Seeds in Maternal soil” and “seeds not in maternal soil”. She used block, maternal id and soil id as random variables. I did that here, but does it make sense?
Response | term | statistic | df | p.value |
Below-ground Dry Weight | (Intercept) | 167.728 | 1 | 0.0 |
MS | 3.543 | 1 | 0.060 | |
Above-ground Dry Weight | (Intercept) | 56.999 | 1 | 0.0 |
MS | 1.621 | 1 | 0.203 | |
Below-ground Wet Weight | (Intercept) | 43.541 | 1 | 0.0 |
MS | 2.103 | 1 | 0.147 | |
Above-ground Wet Weight | (Intercept) | 29.662 | 1 | 0.0 |
MS | 1.648 | 1 | 0.199 | |
Total Biomass (all samples) | (Intercept) | 38.001 | 1 | 0.0 |
MS | 2.067 | 1 | 0.151 | |
Total Biomass (no burnt samples) | (Intercept) | 134.292 | 1 | 0.0 |
MS | 0.703 | 1 | 0.402 |